基於螞蟻最佳化和K-means方法之人臉辨識

A Face Recognition Method Based on the Ant Colony Optimization and K-means Algorithm

林泳輝(Yong-Hui Lin)

指導教授 ： 涂世雄

中原大學/工學院/電機工程研究所/碩士(2013年)

https://doi.org/10.6840/cycu201300868

摘要

在本論文中，我們提出基於螞蟻最佳化和K-means理論的人臉辨識方法，主要目的是希望藉由這個系統，能在複雜環境下進行人臉偵測，並有效提高人臉辨識的正確率。

在本論文中，我們會介紹人臉辨識的流程，首先介紹人臉偵測，我們透過Adaboost的積分影像方式來提升訓練和偵測速度，並結合Adaboost訓練出來的分類器，以串聯的方式組合成一個級聯結構的層壘分類器，此結構可快速排除非人臉樣本，進而達到快速人臉偵測。第二步，介紹特徵擷取，我們透過主成分分析轉換，可有效降低影像維度，並保留變化大的影像特徵，搭配灰階轉換與直方圖等化，使影像中的計算量減少及光源平均化，有效且明顯提升影像特徵。第三步，介紹人臉辨識，我們提出螞蟻最佳化結合K-means理論，透過螞蟻最佳化改善K-means陷入局部極小值的缺點，以改善K-means分類和人臉辨識正確率。

最後，我們會用實驗來證明這個系統的可行性。我們利用Matlab來進行實驗的模擬，我們輸入有人臉的影像，並且經由人臉辨識系統得到結果。實驗結果顯示，成功地達到人臉辨識即便這是個複雜的環境。我們確實提升了人臉辨識上的正確率。
本論文中我們的研究有下列幾點貢獻：
1.範用性：利用Adaboost系統，可在複雜環境下進行臉部偵測。
2.改良性：我們克服傳統K-means陷入局部極小值的缺點，進而提升人臉辨識的正確率。
3.拓展性：本研究能夠用於即時的人臉辨識，例如防盜系統。

關鍵字

人臉辨識 ； Adaboost ； 主成分分析 ； 螞蟻最佳化 ； K-means

並列摘要

In this thesis, we propose a face recognition method based on ant colony optimization and K-means theory. The main purpose is to hope that through this system, the face detection can be well completed in the complex environments, and effectively improve the correction rate of face recognition.

In this thesis, we will introduce the process of face recognition. First, the Adaboost face detection method is given: Calculating the integral image through our Adaboost algorithm, the results of integral image shows that we can improve the training speed and detection rate of images. Then through tandem method we can combine a cascade classifier of cascade structure by Adaboost classifier of trained. It can rapidly solve face detection and exclude samples of non-face. Second, the Principal Component Analysis feature extraction method is presented: Through our conversion of PCA, the result of conversion of PCA shows that we can effectively reduce dimensions of image and retain the large variation of image features, and with grayscale conversion and histogram equalization can reduce the computation of image processing and light source averaging, as a result, the feature of the image can be raised effectively and obviously. Third, the ACO-K-means face recognition method is proposed: we propose ant colony optimization combining the theory of K-means to improve the disadvantage of local minimum in K-means. The results of ACO-K-means improve the correct rate of K-means classification and face recognition.
Finally, we prove the feasibility of this system by experiments. We simulate the experiments by Matlab. At first, a face image is input, and then we can get the results by the face recognition system. The experimental results show that it can solve face recognition successfully even in a complex environment. It actually increases the correction rate of face recognition.

In this thesis the contributions of our research are as follows:
1.Interchangeable: In a complex environment, we can carry out face detection by Adaboost system
2.Ameliorative: Ant Colony Optimization overcomes the disadvantage of local minimum in traditional k-means, and improves the correcting rate of face recognition.
3.Expandable: This result of our thesis can be used for real-time face recognition, for example, burglar system.

並列關鍵字

Adaboost ； Principal Component Analysis ； Face Recognition ； Ant Colony Optimization ； K-means

參考文獻

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